Literature DB >> 34518923

Validation of a Semiautomatic Image Analysis Software for the Quantification of Musculoskeletal Tissues.

Mahdi Imani1,2, Ebrahim Bani Hassan1,2, Sara Vogrin1,2, Aaron Samuel Tze Nor Ch'Ng1,2, Nancy E Lane3, Jane A Cauley4, Gustavo Duque5,6.   

Abstract

Accurate quantification of bone, muscle, and their components is still an unmet need in the musculoskeletal field. Current methods to quantify tissue volumes in 3D images are expensive, labor-intensive, and time-consuming; thus, a reliable, valid, and quick application is highly needed. Tissue Compass is a standalone software for semiautomatic segmentation and automatic quantification of musculoskeletal organs. To validate the software, cross-sectional micro-CT scans images of rat femur (n = 19), and CT images of hip and abdomen (n = 100) from the Osteoporotic Fractures in Men (MrOS) Study were used to quantify bone, hematopoietic marrow (HBM), and marrow adipose tissue (MAT) using commercial manual software as a comparator. Also, abdominal CT scans (n = 100) were used to quantify psoas muscle volumes and intermuscular adipose tissue (IMAT) using the same software. We calculated Pearson's correlation coefficients, individual intra-class correlation coefficients (ICC), and Bland-Altman limits of agreement together with Bland-Altman plots to show the inter- and intra-observer agreement between Tissue Compass and commercially available software. In the animal study, the agreement between Tissue Compass and commercial software was r > 0.93 and ICC > 0.93 for rat femur measurements. Bland-Altman limits of agreement was - 720.89 (- 1.5e+04, 13,074.00) for MAT, 4421.11 (- 1.8e+04, 27,149.73) for HBM and - 6073.32 (- 2.9e+04, 16,388.37) for bone. The inter-observer agreement for QCT human study between two observers was r > 0.99 and ICC > 0.99. Bland-Altman limits of agreement was 0.01 (- 0.07, 0.10) for MAT in hip, 0.02 (- 0.08, 0.12) for HBM in hip, 0.05 (- 0.15, 0.25) for bone in hip, 0.02 (- 0.18, 0.22) for MAT in L1, 0.00 (- 0.16, 0.16) for HBM in L1, and 0.02 (- 0.23, 0.27) for bone in L1. The intra-observer agreement for QCT human study between the two applications was r > 0.997 and ICC > 0.99. Bland-Altman limits of agreement was 0.03 (- 0.13, 0.20) for MAT in hip, 0.05 (- 0.08, 0.18) for HBM in hip, 0.05 (- 0.24, 0.34) for bone in hip, - 0.02 (- 0.34, 0.31) for MAT in L1, - 0.14 (- 0.44, 0.17) for HBM in L1, - 0.29 (- 0.62, 0.05) for bone in L1, 0.03 (- 0.08, 0.15) for IMAT in psoas, and 0.02 (- 0.35, 0.38) for muscle in psoas. Compared to a conventional application, Tissue Compass demonstrated high accuracy and non-inferiority while also facilitating easier analyses. Tissue Compass could become the tool of choice to diagnose tissue loss/gain syndromes in the future by requiring a small number of CT sections to detect tissue volumes and fat infiltration.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Image processing; Intramuscular fat; Marrow adipose tissue; Osteoporosis; Osteosarcopenia; Sarcopenia

Mesh:

Year:  2021        PMID: 34518923      PMCID: PMC8863586          DOI: 10.1007/s00223-021-00914-4

Source DB:  PubMed          Journal:  Calcif Tissue Int        ISSN: 0171-967X            Impact factor:   4.333


  32 in total

1.  Overview of recruitment for the osteoporotic fractures in men study (MrOS).

Authors:  Janet Babich Blank; Peggy Mannen Cawthon; Mary Lou Carrion-Petersen; Loretta Harper; J Phillip Johnson; Eileen Mitson; Romelia Ramírez Delay
Journal:  Contemp Clin Trials       Date:  2005-10       Impact factor: 2.226

2.  Fat infiltration of muscle, diabetes, and clinical fracture risk in older adults.

Authors:  Anne L Schafer; Eric Vittinghoff; Thomas F Lang; Deborah E Sellmeyer; Tamara B Harris; Alka M Kanaya; Elsa S Strotmeyer; Peggy M Cawthon; Steven R Cummings; Frances A Tylavsky; Ann L Scherzinger; Ann V Schwartz
Journal:  J Clin Endocrinol Metab       Date:  2010-07-28       Impact factor: 5.958

Review 3.  Quantitative imaging techniques for the assessment of osteoporosis and sarcopenia.

Authors:  Sara Guerri; Daniele Mercatelli; Maria Pilar Aparisi Gómez; Alessandro Napoli; Giuseppe Battista; Giuseppe Guglielmi; Alberto Bazzocchi
Journal:  Quant Imaging Med Surg       Date:  2018-02

4.  Age-related bone loss in the LOU/c rat model of healthy ageing.

Authors:  Gustavo Duque; Daniel Rivas; Wei Li; Ailian Li; Janet E Henderson; Guylaine Ferland; Pierrette Gaudreau
Journal:  Exp Gerontol       Date:  2008-10-21       Impact factor: 4.032

Review 5.  Body weight, anorexia, and undernutrition in older people.

Authors:  Stijn Soenen; Ian M Chapman
Journal:  J Am Med Dir Assoc       Date:  2013-03-19       Impact factor: 4.669

6.  Sarcopenia Definition: The Position Statements of the Sarcopenia Definition and Outcomes Consortium.

Authors:  Shalender Bhasin; Thomas G Travison; Todd M Manini; Sheena Patel; Karol M Pencina; Roger A Fielding; Jay M Magaziner; Anne B Newman; Douglas P Kiel; Cyrus Cooper; Jack M Guralnik; Jane A Cauley; Hidenori Arai; Brian C Clark; Francesco Landi; Laura A Schaap; Suzette L Pereira; Daniel Rooks; Jean Woo; Linda J Woodhouse; Ellen Binder; Todd Brown; Michelle Shardell; Quian-Li Xue; Ralph B DʼAgostino; Denise Orwig; Greg Gorsicki; Rosaly Correa-De-Araujo; Peggy M Cawthon
Journal:  J Am Geriatr Soc       Date:  2020-03-09       Impact factor: 5.562

7.  Marrow Adipose Tissue in Older Men: Association with Visceral and Subcutaneous Fat, Bone Volume, Metabolism, and Inflammation.

Authors:  Ebrahim Bani Hassan; Oddom Demontiero; Sara Vogrin; Alvin Ng; Gustavo Duque
Journal:  Calcif Tissue Int       Date:  2018-03-26       Impact factor: 4.333

8.  Sarcopenia Definitions and Outcomes Consortium (SDOC) Criteria are Strongly Associated With Malnutrition, Depression, Falls, and Fractures in High-Risk Older Persons.

Authors:  Ben Kirk; Jesse Zanker; Ebrahim Bani Hassan; Stefanie Bird; Sharon Brennan-Olsen; Gustavo Duque
Journal:  J Am Med Dir Assoc       Date:  2020-08-06       Impact factor: 4.669

9.  Adipocytic proportion of bone marrow is inversely related to bone formation in osteoporosis.

Authors:  S Verma; J H Rajaratnam; J Denton; J A Hoyland; R J Byers
Journal:  J Clin Pathol       Date:  2002-09       Impact factor: 3.411

10.  Test-Retest Reliability of the Assessment of Fatty Liver Disease Using Low-Dose Computed Tomography in Cardiac Patients.

Authors:  Antti Hokkanen; Hanna Hämäläinen; Tiina M Laitinen; Tomi P Laitinen
Journal:  Front Med (Lausanne)       Date:  2021-04-15
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